Secure Multi-party Computation Analysis

Secure Multi-Party Computation Analysis is a formal academic concept referring to a computational framework within encrypted data analysis that enables multiple parties to jointly evaluate a function over their private inputs while preserving data confidentiality, distinguished by its reliance on cryptographic protocols that ensure no party learns more than the output, thus enabling privacy-preserving collaborative analysis without trusted third parties.

32

Publications

2.8K

Citations

78

Authors

45

Institutions

Publications per year

2017–2026

13

Authors

78

  1. ES
    Elaine Shi

    Cornell University, United States

    3 publications · 289 citations

    3

    h-index

  2. MT
    Michael T. Goodrich

    Johns Hopkins University, United States

    2 publications · 101 citations

    2

    h-index

  3. XW
    Xiao Wang

    University of Maryland, College Park, United States

    2 publications · 261 citations

    2

    h-index

  4. MR
    Mike Rosulek

    Oregon State University, United States

    2 publications · 424 citations

    2

    h-index

  5. IA
    Isaac Agudo

    Universidad de Málaga, Spain

    1 publications · 60 citations

    1

    h-index

1–5 of 78

Venues

  1. Information Sciences

    2 publications · 522 citations

    2

    h-index

  2. International Journal of Information Security

    1 publications · 104 citations

    1

    h-index

  3. Entropy

    1 publications · 12 citations

    1

    h-index

  4. Computers & Security

    1 publications · 56 citations

    1

    h-index

  5. IACR Cryptology ePrint Archive

    Repository · 1 publications · 15 citations

    1

    h-index

1–5